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Reconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficient

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dc.citedby38
dc.contributor.authorMohamed Salleh F.H.en_US
dc.contributor.authorArif S.M.en_US
dc.contributor.authorZainudin S.en_US
dc.contributor.authorFirdaus-Raih M.en_US
dc.contributor.authorid26423229000en_US
dc.contributor.authorid26646287700en_US
dc.contributor.authorid24479069300en_US
dc.contributor.authorid57221461047en_US
dc.date.accessioned2023-05-29T05:59:39Z
dc.date.available2023-05-29T05:59:39Z
dc.date.issued2015
dc.descriptionBioinformatics; Correlation methods; Gaussian distribution; Gaussian noise (electronic); Genes; DREAM; Gaussian model; Gene regulatory networks; Pearson correlation coefficients; Probability and statistics; Complex networks; algorithm; biological model; biology; gene inactivation; gene regulatory network; genetics; human; Algorithms; Computational Biology; Gene Knockout Techniques; Gene Regulatory Networks; Humans; Models, Geneticen_US
dc.description.abstractA gene regulatory network (GRN) is a large and complex network consisting of interacting elements that, over time, affect each other's state. The dynamics of complex gene regulatory processes are difficult to understand using intuitive approaches alone. To overcome this problem, we propose an algorithm for inferring the regulatory interactions from knock-out data using a Gaussian model combines with Pearson Correlation Coefficient (PCC). There are several problems relating to GRN construction that have been outlined in this paper. We demonstrated the ability of our proposed method to (1) predict the presence of regulatory interactions between genes, (2) their directionality and (3) their states (activation or suppression). The algorithm was applied to network sizes of 10 and 50 genes from DREAM3 datasets and network sizes of 10 from DREAM4 datasets. The predicted networks were evaluated based on AUROC and AUPR. We discovered that high false positive values were generated by our GRN prediction methods because the indirect regulations have been wrongly predicted as true relationships. We achieved satisfactory results as the majority of sub-networks achieved AUROC values above 0.5. � 2015 Elsevier Ltd. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.1016/j.compbiolchem.2015.04.012
dc.identifier.epage14
dc.identifier.scopus2-s2.0-84939857009
dc.identifier.spage3
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84939857009&doi=10.1016%2fj.compbiolchem.2015.04.012&partnerID=40&md5=78b3c6b08f544453c9465e147acd172b
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22212
dc.identifier.volume59
dc.publisherElsevier Ltden_US
dc.sourceScopus
dc.sourcetitleComputational Biology and Chemistry
dc.titleReconstructing gene regulatory networks from knock-out data using Gaussian Noise Model and Pearson Correlation Coefficienten_US
dc.typeArticleen_US
dspace.entity.typePublication
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